Abstract
With the increasing competition in the banking industry, accurate prediction of banking customer churn has become an important way in managing customer relationships. To explore efficacy features, enhance the generalization performance of customer churn prediction, this study proposed a XGBoost model with feature fusion for banking customer churn prediction. At first, a feature fusion model based on improved RFM and Affinity Propagation clustering was proposed to extract features representing the long-term and dynamic behavior of customers. By integrating different types of features, a XGBoost model was proposed to predict customer churn. Experimental results demonstrate the superior performance of the proposed model in comparison to other benchmark models.
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Acknowledgement
This work was supported by the Major Research Project of the Ministry of Education on Philosophy and Social Sciences (20JZD024), and the 2022 WHU-DKU Joint Seeding Program (XXWHUDKUZZJJ202303).
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Hu, Z., Dong, F., Wu, J., Misir, M. (2024). Prediction of Banking Customer Churn Based on XGBoost with Feature Fusion. In: Tu, Y.P., Chi, M. (eds) E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future. WHICEB 2024. Lecture Notes in Business Information Processing, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-031-60324-2_13
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DOI: https://doi.org/10.1007/978-3-031-60324-2_13
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